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Over the last few decades, the developed countries have witnessed a strong increase in healthcare spending, essentially due to the emergence of new diseases, the aging of the population, and the development of new and costly technologies. The Health Technology Assessment (HTA) is a powerful tool for connecting the technical-scientific and decision-making world, which helps to prevent the introduction of inappropriate, ineffective, or superfluous technologies within the health system, thus limiting the expense, and improving the overall quality of medical care. This paper describes a hospital-based HTA procedure (costs-efficacy evaluation), which has been exploited to compare an innovative device, with integrated camera, for endobronchial intubation in thoracic surgery, with the instrumentation currently in use at the National Cancer Institute “G. Pascale” of Naples, which is the largest Institute of Hospitalization, Scientific Research and Care for the Oncology in Southern Italy. In the case under study, by introducing the new technology, direct variable costs could result in an actual significant spending increase for the “G. Pascale” Institute; however, despite that, the results of the HTA procedure and of the Analysis of Costs Minimization show that the introduction of the innovative device could allow to significantly decrease the intubation time, the overall surgery duration and the costs incurred by the Institute. These results confirm also the usefulness of the hospital based HTA procedures.

Movement disorders: Pediatrics Trastornos del movimiento; Pediatría Trastorns del moviment; Pediatria Background The International Parkinson and Movement Disorders Society (MDS) set up a working group on pediatric movement disorders (MDS Task Force on Pediatrics) to generate recommendations to guide the transition process from pediatrics to adult health care systems in patients with childhood-onset movement disorders. Methods To develop recommendations for transitional care for childhood onset movement disorders, we used a formal consensus development process, using a multi-round, web-based Delphi survey. The Delphi survey was based on the results of the scoping review of the literature and the results of a survey of MDS members on transition practices. Through iterative discussions, we generated the recommendations included in the survey. The MDS Task Force on Pediatrics were the voting members for the Delphi survey. The task force members comprise 23 child and adult neurologists with expertise in the field of movement disorders and from all regions of the world. Results Fifteen recommendations divided across four different areas were made pertaining to: (1) team composition and structure, (2) planning and readiness, (3) goals of care, and (4) administration and research. All recommendations achieved consensus with a median score of 7 or greater. Conclusion Recommendations on providing transitional care for patients with childhood onset movement disorders are provided. Nevertheless several challenges remain in the implementation of these recommendations, related to health infrastructure and the distribution of health resources, and the availability of knowledgeable and interested practitioners. Research on the influence of transitional care programs on outcomes in childhood onset movement disorders is much needed. This study was funded by the International Parkinson's and Movement Disorders Society. The authors have no financial disclosures or conflicts of interest concerning the research related to the manuscript.

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase.

Topological inference of biological interaction networks from experimental data is a fundamental research topic in the broad area of Systems Biology. Several algorithms presented in the literature have been devised in order to infer the topology of a network from time-course data. The present work introduces a novel method for reverse-engineering gene regulatory networks from time-course experiments, which combines the instrumental variables technique for the identification of dynamical systems with a regularization strategy for dealing with over-parametrized systems. Differently from least squares methods, the proposed approach can explicitly address the bias and nonconsistency issues that arise when dealing with time-course measurements, thus yielding improved performance with respect to methods designed for steady-state data. Moreover, the devised approach, which has been named RIVA (Reverse-engineering of biological networks via Instrumental VAriables), can simultaneously exploit multiple time-series, thus enabling one to get improved results by collecting and exploiting data from multiple experiments, and is computationally efficient, thus it can be also applied to large-scale (in the order of thousands of nodes) networks. To analyze the applicability and effectiveness of RIVA, we performed several tests, both with simulated data and with experimental data, and compared the results against other state-of-the-art inference methods designed for time-series data.

In this study, superparamagnetic iron oxide nanoparticles (SPIONs) were engineered with an organic coating composed of low molecular weight heparin (LMWH) and bovine serum albumin (BSA), providing heparin-based nanoparticle systems (LMWH@SPIONs). The purpose was to merge the properties of the heparin skeleton and an inorganic core to build up a targeted theranostic nanosystem, which was eventually enhanced by loading a chemotherapeutic agent. Iron oxide cores were prepared via the co-precipitation of iron salts in an alkaline environment and oleic acid (OA) capping. Dopamine (DA) was covalently linked to BSA and LMWH by amide linkages via carbodiimide coupling. The following ligand exchange reaction between the DA-BSA/DA-LMWH and OA was conducted in a biphasic system composed of water and hexane, affording LMWH@SPIONs stabilized in water by polystyrene sulfonate (PSS). Their size and morphology were investigated via dynamic light scattering (DLS) and transmission electron microscopy (TEM), respectively. The LMWH@SPIONs’ cytotoxicity was tested, showing marginal or no toxicity for samples prepared with PSS at concentrations of 50 µg/mL. Their inhibitory activity on the heparanase enzyme was measured, showing an effective inhibition at concentrations comparable to G4000 (N-desulfo-N-acetyl heparin, a non-anticoagulant and antiheparanase heparin derivative; Roneparstat). The LMWH@SPION encapsulation of paclitaxel (PTX) enhanced the antitumor effect of this chemotherapeutic on breast cancer cells, likely due to an improved internalization of the nanoformulated drug with respect to the free molecule. Lastly, time-domain NMR (TD-NMR) experiments were conducted on LMWH@SPIONs obtaining relaxivity values within the same order of magnitude as currently used commercial contrast agents.

We compared intrarectal local anesthesia plus pelvic plexus block vs intrarectal local anesthesia plus periprostatic nerve block during transrectal ultrasound guided prostate biopsy.Patients were randomized 1:1 by a computer generated schedule into group 1-90 who received intrarectal local anesthesia (lidocaine 1.5%-nifedipine 0.3% cream) plus pelvic plexus block (2.5 ml lidocaine 1% plus naropine 0.75% injected on each side into the pelvic neurovascular plexus lateral to the seminal vesicle tip) and group 2-90 who received intrarectal local anesthesia plus periprostatic nerve block (2.5 ml of the same mixture injected on each side into the neurovascular bundles at the prostate-bladder-seminal vesicle angle) before transrectal ultrasound guided prostate biopsy. After the procedure patients were instructed to rate the level of pain/discomfort from 0 to 10 on the visual analog scale at certain time points, including during the introduction and presence of the probe in the rectum, during pelvic plexus block or periprostatic nerve block, during biopsy and 30 minutes after biopsy.The 2 groups were similar in age, serum prostate specific antigen and total prostate volume. There was no difference in pain perception during probe introduction and pelvic plexus or periprostatic nerve block. Pain during prostate biopsy was significantly lower in group 1 than in group 2 (p0.001). The same trend was recorded for pain perception 30 minutes after biopsy (p = 0.001). There were no major complications.Pelvic plexus block under Doppler ultrasound guidance provides better analgesia than periprostatic nerve block during office based transrectal ultrasound guided prostate biopsy.

Voice is the result of the coordination of the whole pneumophonoarticulatory apparatus. The analysis of the voice allows the identification of the diseases of the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. The paper presents a web-based system for the acquisition and automatic analysis of vocal signals. Vocal signals are submitted by the users through a simple web-interface and are analyzed in real-time by using state-of-the art signal processing techniques, providing first-level information on possible voice alterations. The system offers different analysis functions to the doctors that may analyze suspected cases in detail. The system is currently being tested in the otorhinolaryngologist setting to carry out mass prevention via screening at a regional scale.

This work deals with the Input-Output Finite-Time Stability (IO-FTS) of linear discrete-time systems. In current control science, discrete-time systems play a very important role in many engineering contexts. Moreover, many discrete-time control problems are defined over a finite interval of time, therefore the development and application of finite-time control methodologies is of particular relevance. The first contribution of the paper is a pair of necessary and sufficient conditions for IO-FTS. The former involves the solution of a set of Generalized Difference Lya-punov Equations (GDLEs); the latter allows one to establish the feasibility of an optimization problem by solving a set of Difference Linear Matrix Inequalities (DLMIs). The second contribution of the paper is a theorem for IO finite-time stabilization via state feedback, followed by a more general one for stabilization via output feedback. Both conditions are necessary and sufficient, and lead to optimization problems cast in the form of DLMIs. The applicability of the devised results is illustrated through a numerical example.

Knowledge-driven and data-driven strategies have been widely used to address many bioengineering and clinical open questions. However, little attention has been paid to the potential advantages the integration of such strategies could open up. To this aim, in this paper, we describe a sequential simulation and machine learning (ML) framework. Firstly, an ad-hoc mathematical model, based on differential equations, was used to simulate - starting from real data - cardiac troponin concentration curves of 27 patients (with Acute Myocardial Infarction and ST-segment elevation) in a 200h time frame; later, the curves were analyzed to extract 4 time-domain features which, fed to 3 tree-based ML algorithms, allowed to successfully classify - ML scores > 75% for Gradient Boosted Tree - patients in two risk classes according to Thrombolysis in Myocardial Infarction risk index. These promising results could stimulate researchers to consider combined knowledge-driven and data-driven strategies to address other cardiovascular and/or clinical research questions.

This paper presents the mechatronic design of a biorobotic joint with controllable compliance, for innovative applications of “assist-as-needed” robotic rehabilitation mediated by a wearable and soft exoskeleton. The soft actuation of robotic exoskeletons can provide some relevant advantages in terms of controllable compliance, adaptivity and intrinsic safety of the control performance of the robot during the interaction with the patient. Pneumatic Artificial Muscles (PAMs), which belong to the class of soft actuators, can be arranged in antagonistic configuration in order to exploit the variability of their mechanical compliance for the optimal adaptation of the robot performance during therapy. The coupling of an antagonistic configuration of PAMs with a regulation mechanism can achieve, under a customized control strategy, the optimal tuning of the mechanical compliance of the exoskeleton joint over full ranges of actuation pressure and joint rotation. This work presents a novel mechanism, for the optimal regulation of the compliance of the biorobotic joint, which is characterized by a soft and hybrid actuation exploiting the storage/release of the elastic energy by bistable Von Mises elastic trusses. The contribution from elastic Von Mises structure can improve both the mechanical response of the soft pneumatic bellows actuating the regulation mechanism and the intrinsic safety of the whole mechanism. A comprehensive set of design steps is presented here, including the optimization of the geometry of the pneumatic bellows, the fabrication process through 3D printing of the mechanism and some experimental tests devoted to the characterization of the hybrid soft actuation. The experimental tests replicated the main operating conditions of the regulation mechanism; the advantages arising from the bistable hybrid soft actuation were evaluated in terms of static and dynamic performance, e.g., pressure and force transition thresholds of the bistable mechanism, linearity and hysteresis of the actuator response.

The dissolution of Bombyx mori silk fibroin (SF) films in formic acid (FA) for the preparation of electrospinning dopes is widely exploited to produce electrospun SF scaffolds. The SILKBridge® nerve conduit is an example of medical device having in its wall structure an electrospun component produced from an FA spinning dope. Though highly volatile, residual FA remains trapped into the bulk of the SF nanofibers. The purpose of this work is to investigate the type and strength of the interaction between FA and SF in electrospun mats, to quantify its amount and to evaluate its possible toxicological impact on human health. The presence of residual FA in SF mats was detected by FTIR and Raman spectroscopy (new carbonyl peak at about 1,725 cm−1) and by solid state NMR, which revealed a new carbonyl signal at about 164.3 ppm, attributed to FA by isotopic 13C substitution. Changes occurred also in the spectral ranges of hydroxylated amino acids (Ser and Thr), demonstrating that FA interacted with SF by forming formyl esters. The total amount of FA was determined by HS-GC/MS analysis and accounted for 247 ± 20 μmol/g. The greatest part was present as formyl ester, a small part (about 3%) as free FA. Approximately 17% of the 1,500 μmol/g of hydroxy amino acids (Ser and Thr) theoretically available were involved in the formation of formyl esters. Treatment with alkali (Na2CO3) succeeded to remove the greatest part of FA, but not all. Alkali-treated electrospun SF mats underwent morphological, physical, and mechanical changes. The average diameter of the fibers increased from about 440 nm to about 480 nm, the mat shrunk, became stiffer (the modulus increased from about 5.5 MPa to about 7 MPa), and lost elasticity (the strain decreased from about 1 mm/mm to about 0.8 mm/mm). Biocompatibility studies with human adult dermal fibroblasts did not show significant difference in cell proliferation (313 ± 18 and 309 ± 23 cells/mm2 for untreated and alkali-treated SF mat, respectively) and metabolic activity. An in-depth evaluation of the possible toxicological impact of residual FA was made using the SILKBridge® nerve conduit as case study, following the provisions of the ISO 10993-1 standard. The Potential Patient Daily Intake, calculated from the total amount of FA determined by HS-GC/MS, was 2.4 mg/day and the Tolerable Exposure level was set to 35.4 mg/day. This allowed to obtain a value of the Margin of Safety of 15, indicating that the amount of FA left on SF mats after electrospinning does not raise concerns for human health.

Invasive alien species threaten biodiversity and ecosystem structure and functioning, but incomplete assessments of their origins and temporal trends impair our ability to understand the relative importance of different factors driving invasion success. Continuous time-series are needed to assess invasion dynamics, but such data are usually difficult to obtain, especially in the case of small-sized taxa that may remain undetected for several decades. In this study, we show how micropaleontologic analysis of sedimentary cores coupled with radiometric dating can be used to date the first arrival and to reconstruct temporal trends of foraminiferal species, focusing on the alien Amphistegina lobifera and its cryptogenic congener A. lessonii in the Maltese Islands. Our results show that the two species had reached the Central Mediterranean Sea several decades earlier than reported in the literature, with considerable implications for all previous hypotheses of their spreading patterns and rates. By relating the population dynamics of the two foraminifera with trends in sea surface temperature, we document a strong relationship between sea warming and population outbreaks of both species. We conclude that the micropaleontologic approach is a reliable procedure for reconstructing the bioinvasion dynamics of taxa having mineralized remains, and can be added to the toolkit for studying invasions., peer-reviewed

Non-covalent interactions in supramolecular chemistry provide useful systems to understand biological processes, and self-assembly systems are suitable assets to build-up innovative products for biomedical applications. In this field, polyelectrolyte complexes are interesting, especially when polysaccharides are involved, due to their non-toxicity and bio-absorbability. In this work, we investigated a polyelectrolyte formed by hyaluronic acid (HA), a negatively charged linear polysaccharide, with Chitlac (Ch), a positively charged lactose-modified chitosan. The aim of the study was the investigation of a novel Ch–HA polyelectrolyte complex, to understand the interaction between the two polysaccharides and the stability towards enzymatic activity. By means of gel permeation chromatography–triple detector array (GPC–TDA), nuclear magnetic resonance (NMR), dynamic viscosity, Zeta Potential and scanning electron microscopy (SEM), the polyelectrolyte complex properties were identified and compared to individual polysaccharides. The complex showed monodisperse molecular weight distribution, high viscosity, negative charge, and could be degraded by specific enzymes, such as hyaluronidase and lysozyme. The results suggest a close interaction between the two polysaccharides in the complex, which could be considered a self-assembly system.

Background and objectives The most advanced technologies and continuous innovations in the medical field require a necessary interaction between the clinical and the engineering world. In this context, software applications are proposed as a bridge between the two scientific fields and, therefore, as powerful tools, easy to use, and with great analytical skills. In this work, we propose CBRA as an innovative software platform, moving towards personalized medicine, which aims to simplify and speed up the triage of patients and support doctors in the diagnostic and prognostic phase. Methods The computational core of the devised software application consists of a model-based identification algorithm, which enables the reconstruction of the cardiac biomarkers release curves in patients with ST-Elevation Acute Myocardial Infarction (STEMI). Identification and parametric optimization techniques allow the application of the proposed approach to each singular patient: based on a few experimental acquisitions, CBRA can extrapolate several quantitative features of high clinical relevance, thus facilitating and rendering more objective the clinical evaluation and therapeutic choices. A dedicated database to collect and manage patients clinical and personal data, as well as a graphical user interface, provides clinicians and researchers with an intuitive and user-friendly environment. Results In the following work, we present some examples of the possible applications of CBRA, ranging from the management of the cardiac biomarkers time-series, up to the real analysis of the clinical features that CBRA can extract from the reconstructed curve, such as, e.g., maximum concentration values of biomarkers in the plasma and relative times, in the distinct phases of the acute myocardial infarction, or identification of the time to onset of symptoms. Conclusions CBRA makes it easy for clinicians to use modeling and parametric identification tools to reconstruct release curves. Furthermore, CBRA provides support to the clinical decision, thanks to its capability to extract information of high clinical relevance, not easily obtainable from the mere visual analysis of experimental samples. Having information about the previously listed clinical parameters could allow, e.g., identify in which stage of AMI the patient is, when She/He goes to the emergency room, with significant benefits in the therapy.

Quantitative analysis of biochemical parameters is crucial for a correct diagnosis and prognosis of patients subject to acute myocardial infarction (AMI). In order to achieve a quantitative understanding of the dynamics of cardiac biomarkers, we have developed a mathematical model that can be exploited to extrapolate the release curve of cardiac troponin T (cTnT) into the plasma from few experimental acquisitions. The present work introduces a novel approach, based on the cTnT-release model, aimed at the identification of the infarct onset time. Indeed, in spite of the clinical importance of such information, in many cases, it is not easy to establish the exact time of occurrence of the ischemic event. We show that using a model-based optimization approach, the infarct onset time can be reliably estimated using the cTnT concentration acquisitions taken in the first few hours post-AMI. The assessment of the proposed approach is conducted on an experimental dataset, in which the infarct has been artificially induced and, therefore, the onset time is exactly known. In particular, the effectiveness of the devised estimation algorithm has been tested under several scenarios, with the first cTnT acquisition taken up to 12 h after AMI. Altogether, the proposed model-based approach provides a useful tool to help the clinicians in the quantitative estimation of important clinical parameters from the release curves of the cardiac biomarkers.

In the acne severity evaluation, the quantification of inflammatory lesions on the patients’ face is a fundamental step. The aim of this work is to develop a method that automatically performs acne lesions counting. We acquired high-resolution multi-polarization images of facial skin using the visible spectrum. 1089 patients with acne grading ranging from clear to severe were imaged from one to four times at two weeks interval. Images from five different point of views were acquired from each subject and converted to a standardized planar representation. Experts annotated these planar images creating the ground truth. Patients were subdivided into training (648), validation (111), and holdout test subsets (330). A U-net architecture was trained for 61 epochs to output a gray-scale image where hyperintensities corresponded to predicted location of potential acne lesion. Subsequently a blob detection algorithm was used to perform the counting of predicted lesion on each patient. Results showed that the correlation coefficient between ground truth and automatized counting was 0.65 (confidence interval (C.I.) 0.61 - 0.69). This result suggests a very good agreement between ground truth and automatized counting, indicating that it is indeed feasible to meet this clinical need using deep learning methods.

In this paper, we investigate some finite-time control problems for stochastic linear time-varying systems, described by an Itô type differential equation. In particular, the annular stochastic finite-time stability control problem is dealt with. In this context, it is required that the norm of the state variables remains bounded between an inner and an outer ellipsoid, during a finite-interval of time. The first contribution of the paper consists of two sufficient conditions for stability, which are derived by exploiting an approach based on time-varying quadratic Lyapunov functions. The first condition requires the solution of two generalised differential Lyapunov equations; the latter the existence of a feasible solution to a pair of differential linear matrix inequalities. As particular cases, we obtain sufficient conditions for stochastic finite-time stability (in this last case, the lower ellipsoid collapses to the origin). From the analysis conditions, a sufficient condition is derived for the annular stochastic finite-time stabilisation via both state and output feedback. Some numerical examples illustrate the application of the proposed methodology and show that our approach attains less conservative results than those obtainable with the existing literature. Moreover, an application of the annular stochastic finite-time stability approach to the stabilisation problem of a satellite with respect to the geomagnetic field is presented.

The Emergency Department (ED) is a zone of a hospital where usually the healthcare staff has to face sudden and serious heathy problems. On the other side, patients find themselves in a situation of great emotion, discomfort and anxiety, aggravated by the expectation that, in cases of lesser severity, it can be long and badly tolerated. Furthermore, during access, patients and caregivers must undertake visits, assessments, consultations and bureaucratic procedures that can be long and complicated, due to inadequate or inaccurate information. In these conditions, waiting times can often be very long and / or patients can decide to abandon the ED. In this work, a combination of important managerial methodologies, Lean Six Sigma and DMAIC cycle (Define, Measure, Analyse, Improve, Control) are employed at an important hospital of South Italy in order to improve the emergency care process as well as the patient experience. It is a retrospective study, for which the historical times of access to the ED were analysed for the years 2015 and 2016. During this period, to improve the reception of patients and to reduce voluntary departures, two digital monitors were being designed and installed in the waiting room of the ED, which allow the visualization of the overall situation at the ED itself. The visualization is of course totally dynamic, i.e. the layout varies according to the data taken into consideration. In conclusion, the adopted approach impacts on the entrance and waiting times of patients in the ED and improves the general patient experience.

Highlights: E200K-PRNP mutation is the most common cause of fCJD. The typical presentation includes rapidly progressive dementia, myoclonus, cerebellar manifestations, and other motor signs. Early sensory disturbances and seizures are infrequent symptoms. We described 4(out of 5) cases of fCJD manifesting ESD and seizures as dominant clinical features. The present findings further underline the clinical variability of fCJD

Urea is a well-known moisturiser and keratolytic topical agent. As it is widely used in dermatology, several formulations at different concentrations have been marketed: lotions, creams, foams, ointments, gels and lacquers. Availability of different vehicles and concentration may vary in different countries, but in general products at low, medium and high urea concentration are accessible worldwide. The proper formulation should be chosen according to the disorder to treat, its severity, body areas involved and patients' preference.

The development of 3D printable hydrogels based on the crosslinking between chitosan and gelatin is proposed. Chitosan and gelatin were both functionalized with methyl furan groups. Chemical modification was performed by reductive amination with methyl furfural involving the lysine residues of gelatin and the amino groups of chitosan to generate hydrogels with tailored properties. The methyl furan residues present in both polymers were exploited for efficient crosslinking via Diels-Alder ligation with PEG-Star-maleimide under cell-compatible conditions. The obtained chitosan-gelatin hybrid was employed to formulate hydrogels and 3D printable biopolymers and its processability and biocompatibility were preliminarily investigated.

13 pages, 11 figures, Cardiovascular diseases represent, to date, the major cause of mortality worldwide. Diagnosis of the most frequent of such disease, acute myocardial infarction (AMI), requires the evaluation of time-series measurement of specific cardiac biomarkers concentration. The aim of this paper is to provide the clinicians with a quantitative tool to analyze such time-series, with the final goal of enhancing the diagnostic and prognostic procedures. The proposed approach is based on a novel dynamical model, which synthetically describes the basic mechanisms underlying cardiac troponin (cTnT) release into the plasma after the onset of AMI. Leveraging tools of system identification and a data set of AMI patients treated at our University Hospital, the model has been assessed as an effective tool to quantify the characteristic release curves observed under different conditions. Furthermore, it has been shown how the devised approach is also suitable in those cases where only partial measurements are available to the clinician to recover important clinical information. Finally, an optimal experimental design analysis has been performed in order to gain insights on how to optimize the experimental data collection phase with potentially relevant implications on both the quality and cost of the diagnosis procedure, M.R.G. acknowledges financial support from the Spanish Government and the European Regional Development Fund through the project DPI2014-54085-JIN

The realization of embedded molecular control systems is a challenging aim in Synthetic Biology, where a major goal is to design synthetic biological circuits performing specific tasks. In this field, the novel emergent approach is to assemble the circuit in a modular fashion, possibly restraining reciprocal interactions from interconnected modules (zero-retroactivity). Within this framework, recent results have been proposed, dealing with the realization of an embedded subtractor module, with the idea of exploiting it in a more general chemical reaction network that resembles a classical control scheme. So far, this research has been carried out according to the deterministic approach. More sophisticated analysis requires the use of stochastic models, which play a paramount role in investigating noise propagation in chemical reaction networks, especially when the species copy number is low and the intrinsic stochasticity of the phenomena under investigation cannot be neglected. This note deals with a first analysis of the subtractor module, according to the stochastic approach. To this end, Chemical Master Equations are exploited to model one of the possible molecular circuits implementing the subtractor, and moment equations are written in order to evaluate how noise propagates with respect to different values of the inputs and different model parameter settings.

This study updates the current distribution, range expansion and establishment status of the non-indigenous species Amphistegina lobifera Larsen, 1976 and other foraminifera that are cryptogenic in the Sicily Channel. Prior to this study, amphisteginids were reported from the Levantine Basin, the Central Mediterranean (Tunisia, Malta, Pelagian islands) and the southern Adriatic Sea. Here, we provide new records documenting a north-western expansion in the Central Mediterranean. In summer-autumn 2017 and spring-summer 2018, we collected algae and sediment samples from shallow coastal habitats along the shores of the Maltese archipelago, southern and north-western Sicily, Pantelleria and the Aegadian islands. Analysis of the foraminiferal assemblages showed that A. lobifera is effectively established around Malta and in southern/south-eastern Sicily, and has reached the oceanographic boundary between the Central and Western Mediterranean. Our results also show that the thermotolerant A. lobifera is at an advanced stage of invasion in the Sicily Channel, probably favoured by a recent rise in Mediterranean sea surface temperatures. New species distribution models are provided for the years 2040–2050 and 2090–2100, indicating that the predicted warming trend will facilitate north-westward migration of Mediterranean amphisteginids along the coast of northern Africa into the Alboran Sea, and deep into the Adriatic Sea.

The control of biochemical processes is a major goal in many fields, encompassing metabolic engineering and synthetic biology. Current approaches are based on ad-hoc designs, whereas a general and modular framework would be highly desirable, in order to exploit the well-assessed methods of control theory. The present work deals with the realization of an important component of control systems, namely the subtractor block. Adopting the framework of Chemical Reaction Networks (CRNs), a subtractor module whose output converges to the difference of the input molecular fluxes is devised. The convergence properties of the proposed CRN subtractor module are investigated via a singular perturbation approach. Finally, its effectiveness is investigated through numerical simulations and a comparison with an existing synthetic biomolecular circuit.

Mercury (Hg) is a highly toxic element for living organisms and is known to bioaccumulate and biomagnify. Here, we analyze the response of benthic foraminifera communities cultured in mesocosm and exposed to different concentrations of Hg. Standard morphological analyses and environmental DNA metabarcoding show evidence that Hg pollution has detrimental effects on benthic foraminifera. The molecular analysis provides a more complete view of foraminiferal communities including the soft-walled single-chambered monothalamiids and small-sized hard-shelled rotaliids and textulariids than the morphological one. Among these taxa that are typically overlooked in morphological studies we found potential bioindicators of Hg pollution. The mesocosm approach proves to be an effective method to study benthic foraminiferal responses to various types and concentrations of pollutants over time. This study further supports foraminiferal metabarcoding as a complementary and/or alternative method to standard biomonitoring program based on the morphological identification of species communities.

In this study, we trained a neural network to perform automatic grading of digital images of acne patients with reliabilities comparable or superior to those of expert physicians. A dedicated device was employed to acquire images of 479 patients belonging to three different ethnic groups. A convolutional neural network trained with features extracted from local patches extracted from the facial skin showed an accuracy of 0.85 and a correlation between manual evaluation and automatized IGA of r=0.96. This is the first work where a neural network was able to directly classify acne patients according to an ordinal scale with no human intervention and no need to count lesions.